Urllib3 Vulnerabilities In Weights & Biases

Are you using the Weights & Biases (wandb) library in your Python projects? If so, it's crucial to be aware of recent security findings that could impact your applications. Specifically, the wandb-0.18.7 package, a popular tool for experiment tracking and visualization, has been found to have vulnerabilities stemming from its dependency on urllib3. These aren't just minor hiccups; they're high-severity issues with a CVSS score of 8.6, meaning they pose a significant risk to your system's availability. Let's dive into what these vulnerabilities are, why they matter, and most importantly, how you can protect your projects.

Understanding the Vulnerabilities: CVE-2025-66471 and CVE-2025-66418

The security concerns revolve around two specific Common Vulnerabilities and Exposures (CVEs): CVE-2025-66471 and CVE-2025-66418. Both of these critical vulnerabilities are found within the urllib3 library, which wandb relies on. urllib3 is a fundamental component in many Python projects, providing robust HTTP client functionality, including thread-safe connection pooling and file posting. When a widely used library like wandb depends on a component with security flaws, it creates a ripple effect, potentially exposing all its users to these risks. The wandb-0.18.7 package, in conjunction with urllib3-2.5.0, is the specific combination flagged in this security alert. The vulnerability report indicates that these issues are transitive, meaning they are inherited through the sentry_sdk dependency, which in turn depends on urllib3.

CVE-2025-66471: The Streaming API Weakness

Let's first tackle CVE-2025-66471. This vulnerability specifically targets the streaming API within urllib3. The streaming API is designed to handle large HTTP responses efficiently by reading data in chunks rather than loading the entire content into memory at once. This is a common and necessary feature for processing big datasets or large files. The problem arises when urllib3 deals with highly compressed data. In such scenarios, the library can perform decoding or decompression based on the Content-Encoding header. The flaw lies in the logic where urllib3 might fully decode a small amount of highly compressed data in a single operation, but then inadvertently hold onto a significant portion of the decompressed data in an internal buffer. This can lead to a devastating impact on system resources, consuming excessive CPU cycles and allocating massive amounts of memory. Imagine trying to stream a file, and instead of getting manageable chunks, your system grinds to a halt under the weight of decompressed data it wasn't prepared for. This vulnerability is particularly concerning because it directly impacts the availability of your application, potentially leading to denial-of-service conditions. The exploit maturity is listed as 'Not Defined', but the CVSS score of 8.6 (High) signals a serious threat. The vulnerability was published on December 5th, 2025, and the suggested fix is to upgrade urllib3 to version 2.6.0 or later.

CVE-2025-66418: The Decompression Chain Flaw

Next, we have CVE-2025-66418, another high-severity vulnerability (also with a CVSS score of 8.6) affecting urllib3. This issue is related to how urllib3 handles decompression chains. Specifically, in versions 1.24 up to (but not including) 2.6.0, the number of links in the decompression chain was unbounded. What does this mean in practical terms? It means a malicious server could potentially insert an unlimited number of compression steps into the data it sends. When urllib3 attempts to decompress this data, it gets stuck in a loop or a very long chain of decompression operations. Just like with CVE-2025-66471, this leads to extreme resource consumption, characterized by high CPU usage and massive memory allocation. Essentially, the system becomes overwhelmed trying to unpack data that's been excessively compressed. This creates a significant availability risk, making your application unresponsive or completely inaccessible. The vulnerability was also published on December 5th, 2025, and the recommended remediation is the same: upgrading urllib3 to version 2.6.0 or higher. Both vulnerabilities share the same severe impact on system availability and are classified as High severity with a CVSS score of 8.6, emphasizing the urgency of addressing them.

Why These Vulnerabilities Matter for Weights & Biases Users

Your Weights & Biases (wandb) installation is likely built upon a foundation of various Python libraries, each serving a specific purpose. When you install wandb, you're not just getting the core functionality; you're also pulling in a set of dependencies, and those dependencies might have their own dependencies – a concept known as a dependency chain. In this case, wandb-0.18.7 (the version in question) depends on sentry_sdk, which in turn depends on urllib3. The urllib3-2.5.0 version used here is the culprit. Because urllib3 is such a fundamental library for network communication in Python, flaws within it can have widespread implications. The vulnerabilities we've discussed, CVE-2025-66471 and CVE-2025-66418, are critical because they directly impact the availability of your system. A successful exploit could render your application unresponsive, preventing you from logging experiments, visualizing results, or accessing any of the invaluable features that wandb provides. This is especially problematic in machine learning workflows where consistent data logging and access are paramount for reproducible research and efficient development. Losing access to wandb mid-project could mean lost progress, corrupted experiment logs, and significant delays. The fact that these are transitive vulnerabilities means that even if you didn't explicitly install urllib3 yourself, it got pulled in as a requirement for other packages you did install, like sentry_sdk, which is itself a dependency of wandb. This highlights the importance of keeping all your project's dependencies, direct and indirect, up-to-date and secure.

The Path to Remediation: Upgrading Urllib3

The good news is that there is a clear and actionable solution to these high-severity vulnerabilities. The security reports clearly indicate that the fix for both CVE-2025-66471 and CVE-2025-66418 is available in urllib3 version 2.6.0. Therefore, the primary remediation step is to ensure that your project is using a version of urllib3 that is 2.6.0 or higher. Since urllib3 is a transitive dependency of wandb through sentry_sdk, simply upgrading wandb might not be enough if it doesn't automatically pull in the patched urllib3 version. The most reliable approach is to directly manage your dependencies. You can achieve this by updating your requirements.txt file or your pyproject.toml (if you're using tools like Poetry or PDM). Explicitly specify a compatible version of urllib3 that resolves these issues. For instance, you might add urllib3>=2.6.0 to your requirements. After updating your dependency file, you'll need to reinstall your project's dependencies. If you're using pip, you would typically run pip install --upgrade -r requirements.txt. If you are using a different package manager, consult its documentation for the appropriate upgrade command. It's also wise to run a security scan after updating to confirm that the vulnerabilities have been successfully remediated. Tools that can perform these scans include the one that generated this report, or others like Safety or Dependabot.

Best Practices for Dependency Management and Security

Securing your Python projects goes beyond just fixing immediate vulnerabilities; it requires adopting a proactive approach to dependency management. The discovery of these issues in wandb and its transitive dependencies is a perfect reminder of this. Regularly auditing your project's dependencies is paramount. Tools like the one that generated this report, whitesource-resources.whitesourcesoftware.com, or open-source alternatives like Safety (pip install safety && safety check -r requirements.txt) or Dependabot (integrated with GitHub) can automate the process of scanning your dependencies for known vulnerabilities. Keeping your dependencies updated is another critical practice. While it's tempting to stick with older, stable versions, this can leave your project exposed to newly discovered security flaws. Establish a regular update schedule for your libraries, testing thoroughly after each update to ensure compatibility. When upgrading, pay close attention to semantic versioning (SemVer). A major version bump (e.g., from 2.x.x to 3.x.x) often indicates breaking changes that might require code modifications. Minor and patch updates are generally safer but should still be tested. Finally, understand your dependency tree. Use tools like pipdeptree (pip install pipdeptree && pipdeptree) to visualize which libraries are pulling in others. This helps you identify potential risks and understand the full scope of your project's dependencies, as demonstrated by the sentry_sdk being the intermediary for the urllib3 vulnerability in this case. By integrating these practices into your development workflow, you can significantly reduce your exposure to security threats and maintain a more robust and secure software supply chain.

Conclusion: Staying Secure in a Connected Ecosystem

The vulnerabilities found in urllib3 and impacting wandb-0.18.7 (specifically through its dependency on urllib3-2.5.0) serve as a critical reminder of the interconnected nature of modern software development. The high severity (8.6 CVSS score) of CVE-2025-66471 and CVE-2025-66418, both related to resource exhaustion via manipulated compressed data, underscores the importance of vigilance. Fortunately, the fix is readily available: upgrading urllib3 to version 2.6.0 or higher. For users of wandb, this means actively managing your dependencies to ensure you're not running on vulnerable versions. By prioritizing regular security audits, staying on top of library updates, and understanding your project's dependency graph, you can effectively mitigate risks and protect your applications. Don't let security vulnerabilities derail your machine learning projects.

For further information on vulnerability management and best practices, consider exploring resources from trusted organizations:

• National Vulnerability Database (NVD): For comprehensive details on CVEs. National Vulnerability Database
• OWASP (Open Web Application Security Project): For broader web security best practices. OWASP
• Snyk: A popular platform for vulnerability scanning and management. Snyk